Best 34 Data Quality Tools for Databricks SQL (2026)

Native Databricks data quality: expectations in Lakeflow pipelines warn on, drop, or fail invalid records, and data profiling under Unity Catalog data quality monitoring tracks table metrics and drift over time.

Open-source Databricks Labs data quality framework that defines and runs data quality checks in PySpark pipelines and notebooks on Databricks, batch and streaming, with Lakeflow (DLT) pipeline integration.

Data quality control layer that connects to a Databricks SQL warehouse or cluster, or runs natively on Databricks through a Databricks Asset Bundle, profiling tables and running rule and anomaly scans.

Data + AI observability platform that connects to Databricks through SQL warehouses with a service principal or PAT, reads system tables for lineage, query logs, and Workflows, and runs monitors as SQL queries.

Autonomous data and AI platform that connects to a Databricks SQL warehouse or cluster over JDBC with a token or service principal, and runs profiling and quality checks on Spark or pushdown engines.

Autonomous data quality monitoring platform that monitors Databricks tables across SQL warehouses, Hive Metastore, and Spark, uses Unity Catalog metadata for observability and lineage, and pushes results into Unity Catalog.

Open-source dbt package by Boomi that connects to Databricks to a SQL warehouse with a service principal or PAT, reads system tables and table history for volume and freshness monitors, and reads dbt results from its schema.

Open-source data quality operations center that connects to Databricks through JDBC to a SQL warehouse with an access token, imports tables, and runs profiling and data observability checks on them.

Zero-code data quality software that generates Databricks-native expectations for the Databricks validation engine through an SDK in preview, and runs pushdown matching inside Databricks.

Data observability platform by Datadog that connects to Databricks through a SQL warehouse with a service principal or PAT, monitors tables, and reads query history and column lineage system tables.

Agentic data quality and observability platform that connects to Databricks to a SQL warehouse with a service principal or PAT, runs validators on sources, and builds lineage from Unity Catalog and query history.

AI-native data quality platform that runs profiling and data quality checks as Spark SQL queries or on your existing Spark compute, without moving data out of Databricks.

Data quality product by Coalesce, built on SYNQ, that connects to Databricks with a workspace URL, warehouse ID, and service principal or PAT, runs its monitoring queries on that SQL warehouse, and reads Unity Catalog metadata and query logs for lineage.

Enterprise data observability platform that connects to Databricks with a PAT, workspace URL, and HTTP path to a SQL warehouse or cluster, for Unity Catalog and Hive Metastore tables, views, and materialized views.

AI-based data observability platform that connects to Databricks with a host, HTTP path, and token to a cluster, creates assets from tables or custom SQL, and can read Unity Catalog directly from Spark jobs.

Open-source data quality testing tools that connects to a Databricks SQL warehouse with a personal access token or service principal and pushes read-only SQL queries to profile, test, and monitor tables.

AI-powered data operations center with data and pipeline observability, cross-platform pipeline lineage, and automated root-cause analysis.

Managed agentic data platform built on OpenMetadata that connects to Databricks with a host, token, and HTTP path, ingests catalog metadata, reads query history system tables for usage and lineage, and schedules profiler workflows.

Open-source context platform that connects with a host, token, and HTTP path, ingests catalog metadata, reads query history system tables for usage and lineage, and schedules profiler workflows.

Agentic data trust platform that connects over JDBC to a SQL warehouse with a PAT or service principal, and with SQL pushdown runs profiling and data quality evaluation in Databricks.

Managed data and AI control plane by Boomi that connects to Databricks to a SQL warehouse with a service principal or PAT, reads system tables and table history for volume and freshness monitors, and reads dbt results from its schema.

Enterprise AI trust platform that connects to Databricks with a host, HTTP path, and token or service principal, then profiles tables to set metrics and thresholds automatically.

Data and AI observability control plane that connects to Databricks to a SQL warehouse with a service principal token, runs monitors with only SELECT, SHOW, and DESCRIBE statements, and builds lineage from system tables.

Data observability, quality, and lineage platform that connects to Databricks with a workspace URL, PAT, and SQL warehouse path, ingests all catalogs, and runs freshness, volume, and custom SQL monitors.

Self-hosted data observability platform with value-level data validation, column-level lineage, a data catalog, and a business glossary.

AI-native data quality platform built on data contracts that connects to Databricks SQL, or scans Spark DataFrames inside Databricks pipelines.

Open-source data contracts engine that connects to Databricks SQL, or scans Spark DataFrames inside Databricks pipelines.

AI-powered data engineering platform that connects to Databricks to a SQL warehouse with an OAuth service principal or PAT for data diffs, using a temporary schema for scratch tables.

AI data review agent for dbt pull requests that connects to Databricks to a SQL warehouse with a PAT or service principal and queries it directly to compare base and current environments.

Open-source command-line tool for ODCS data contracts that tests Databricks tables with a token or a service principal's client ID and secret, plus the SQL warehouse HTTP path.

Data product marketplace that reads Databricks Unity Catalog metadata through the REST API with a service principal, writes tags and comments back, and grants read access to approved consumers.

Open-source Python data quality framework that connects to Databricks SQL with add_databricks_sql() using a token, HTTP path, catalog, and schema, then validates tables or SQL queries as batches of data.

Open-source data validation library by Posit that validates Databricks tables through its Ibis backend, installed with the databricks extra.

Data tests built into open-source dbt Core that run as SQL in Databricks SQL through the dbt-databricks adapter: generic tests such as unique and not_null, custom SQL tests, and unit tests.